Overview

Dataset statistics

Number of variables15
Number of observations2291
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory419.9 KiB
Average record size in memory187.7 B

Variable types

Categorical2
Numeric13

Warnings

Name has a high cardinality: 2291 distinct values High cardinality
Meropenem is highly correlated with Piperacillin-tazobactam and 5 other fieldsHigh correlation
Piperacillin-tazobactam is highly correlated with Meropenem and 6 other fieldsHigh correlation
Aztreonam is highly correlated with Ceftriaxone and 3 other fieldsHigh correlation
Ceftriaxone is highly correlated with Aztreonam and 3 other fieldsHigh correlation
Ceftaroline is highly correlated with Aztreonam and 3 other fieldsHigh correlation
Cefepime is highly correlated with Aztreonam and 3 other fieldsHigh correlation
Ampicillin-sulbactam is highly correlated with Piperacillin-tazobactamHigh correlation
Ceftazidime-avibactam is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftobiprole is highly correlated with Ceftolozane-tazobactamHigh correlation
Imipenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftolozane-tazobactam is highly correlated with Meropenem and 6 other fieldsHigh correlation
Doripenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftazidime is highly correlated with Aztreonam and 3 other fieldsHigh correlation
MeroRPX7009_fixed8 is highly correlated with Meropenem and 5 other fieldsHigh correlation
Meropenem is highly correlated with Piperacillin-tazobactam and 5 other fieldsHigh correlation
Piperacillin-tazobactam is highly correlated with Meropenem and 6 other fieldsHigh correlation
Aztreonam is highly correlated with Ceftriaxone and 2 other fieldsHigh correlation
Ceftriaxone is highly correlated with Aztreonam and 4 other fieldsHigh correlation
Ceftaroline is highly correlated with CeftriaxoneHigh correlation
Cefepime is highly correlated with Aztreonam and 1 other fieldsHigh correlation
Ampicillin-sulbactam is highly correlated with Piperacillin-tazobactamHigh correlation
Ceftazidime-avibactam is highly correlated with Meropenem and 6 other fieldsHigh correlation
Ceftobiprole is highly correlated with Ceftriaxone and 1 other fieldsHigh correlation
Imipenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftolozane-tazobactam is highly correlated with Meropenem and 7 other fieldsHigh correlation
Doripenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftazidime is highly correlated with Aztreonam and 3 other fieldsHigh correlation
MeroRPX7009_fixed8 is highly correlated with Meropenem and 5 other fieldsHigh correlation
Meropenem is highly correlated with Piperacillin-tazobactam and 5 other fieldsHigh correlation
Piperacillin-tazobactam is highly correlated with Meropenem and 5 other fieldsHigh correlation
Aztreonam is highly correlated with Ceftriaxone and 2 other fieldsHigh correlation
Ceftriaxone is highly correlated with Aztreonam and 3 other fieldsHigh correlation
Ceftaroline is highly correlated with CeftriaxoneHigh correlation
Cefepime is highly correlated with AztreonamHigh correlation
Ampicillin-sulbactam is highly correlated with Piperacillin-tazobactamHigh correlation
Ceftazidime-avibactam is highly correlated with Meropenem and 4 other fieldsHigh correlation
Ceftobiprole is highly correlated with CeftriaxoneHigh correlation
Imipenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftolozane-tazobactam is highly correlated with Meropenem and 5 other fieldsHigh correlation
Doripenem is highly correlated with Meropenem and 5 other fieldsHigh correlation
Ceftazidime is highly correlated with Aztreonam and 1 other fieldsHigh correlation
MeroRPX7009_fixed8 is highly correlated with Meropenem and 3 other fieldsHigh correlation
Imipenem is highly correlated with MeroRPX7009_fixed8 and 4 other fieldsHigh correlation
Ceftaroline is highly correlated with Cefepime and 5 other fieldsHigh correlation
Cefepime is highly correlated with Ceftaroline and 5 other fieldsHigh correlation
Ceftazidime is highly correlated with Ceftaroline and 7 other fieldsHigh correlation
MeroRPX7009_fixed8 is highly correlated with Imipenem and 5 other fieldsHigh correlation
Aztreonam is highly correlated with Ceftaroline and 5 other fieldsHigh correlation
Doripenem is highly correlated with Imipenem and 5 other fieldsHigh correlation
Ceftazidime-avibactam is highly correlated with Ceftazidime and 5 other fieldsHigh correlation
Ceftolozane-tazobactam is highly correlated with Imipenem and 8 other fieldsHigh correlation
Meropenem is highly correlated with Imipenem and 5 other fieldsHigh correlation
Ceftobiprole is highly correlated with Ceftaroline and 7 other fieldsHigh correlation
Ceftriaxone is highly correlated with Ceftaroline and 4 other fieldsHigh correlation
Piperacillin-tazobactam is highly correlated with Imipenem and 5 other fieldsHigh correlation
Ampicillin-sulbactam is highly correlated with Ceftaroline and 6 other fieldsHigh correlation
Name is uniformly distributed Uniform
Name has unique values Unique
Ceftobiprole has 173 (7.6%) zeros Zeros
Ceftolozane-tazobactam has 174 (7.6%) zeros Zeros
MeroRPX7009_fixed8 has 176 (7.7%) zeros Zeros

Reproduction

Analysis started2021-06-07 23:41:32.686440
Analysis finished2021-06-07 23:42:09.328057
Duration36.64 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2291
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size169.4 KiB
Sentry-2017-1002212
 
1
Sentry-2017-1024266
 
1
Sentry-2016-981689
 
1
Sentry-2017-1013040
 
1
Sentry-2018-1077612
 
1
Other values (2286)
2286 

Length

Max length19
Median length19
Mean length18.6416412
Min length18

Characters and Unicode

Total characters42708
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2291 ?
Unique (%)100.0%

Sample

1st rowSentry-2016-933582
2nd rowSentry-2016-934664
3rd rowSentry-2016-934829
4th rowSentry-2016-934925
5th rowSentry-2016-934954

Common Values

ValueCountFrequency (%)
Sentry-2017-10022121
 
< 0.1%
Sentry-2017-10242661
 
< 0.1%
Sentry-2016-9816891
 
< 0.1%
Sentry-2017-10130401
 
< 0.1%
Sentry-2018-10776121
 
< 0.1%
Sentry-2016-9803671
 
< 0.1%
Sentry-2016-9682841
 
< 0.1%
Sentry-2017-10141111
 
< 0.1%
Sentry-2016-9403611
 
< 0.1%
Sentry-2018-10444231
 
< 0.1%
Other values (2281)2281
99.6%

Length

2021-06-07T18:42:09.722061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sentry-2016-9692241
 
< 0.1%
sentry-2018-10801751
 
< 0.1%
sentry-2017-10148461
 
< 0.1%
sentry-2018-10709951
 
< 0.1%
sentry-2018-10607991
 
< 0.1%
sentry-2017-10350721
 
< 0.1%
sentry-2018-10841531
 
< 0.1%
sentry-2017-10187801
 
< 0.1%
sentry-2017-10012301
 
< 0.1%
sentry-2018-10783861
 
< 0.1%
Other values (2281)2281
99.6%

Most occurring characters

ValueCountFrequency (%)
14851
11.4%
04752
11.1%
-4582
10.7%
23428
 
8.0%
S2291
 
5.4%
e2291
 
5.4%
n2291
 
5.4%
t2291
 
5.4%
r2291
 
5.4%
y2291
 
5.4%
Other values (7)11349
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24380
57.1%
Lowercase Letter11455
26.8%
Dash Punctuation4582
 
10.7%
Uppercase Letter2291
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
14851
19.9%
04752
19.5%
23428
14.1%
82099
8.6%
72051
8.4%
61948
8.0%
91762
 
7.2%
41195
 
4.9%
31149
 
4.7%
51145
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
e2291
20.0%
n2291
20.0%
t2291
20.0%
r2291
20.0%
y2291
20.0%
Uppercase Letter
ValueCountFrequency (%)
S2291
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common28962
67.8%
Latin13746
32.2%

Most frequent character per script

Common
ValueCountFrequency (%)
14851
16.7%
04752
16.4%
-4582
15.8%
23428
11.8%
82099
7.2%
72051
7.1%
61948
6.7%
91762
 
6.1%
41195
 
4.1%
31149
 
4.0%
Latin
ValueCountFrequency (%)
S2291
16.7%
e2291
16.7%
n2291
16.7%
t2291
16.7%
r2291
16.7%
y2291
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII42708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14851
11.4%
04752
11.1%
-4582
10.7%
23428
 
8.0%
S2291
 
5.4%
e2291
 
5.4%
n2291
 
5.4%
t2291
 
5.4%
r2291
 
5.4%
y2291
 
5.4%
Other values (7)11349
26.6%

Meropenem
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.659100829
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:09.911559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.262395627
Coefficient of variation (CV)0.7531930877
Kurtosis-1.53130696
Mean5.659100829
Median Absolute Deviation (MAD)1
Skewness0.5178590168
Sum12965
Variance18.16801648
MonotonicityNot monotonic
2021-06-07T18:42:10.108063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2900
39.3%
12454
19.8%
3294
 
12.8%
11135
 
5.9%
10118
 
5.2%
178
 
3.4%
975
 
3.3%
473
 
3.2%
756
 
2.4%
855
 
2.4%
Other values (2)53
 
2.3%
ValueCountFrequency (%)
178
 
3.4%
2900
39.3%
3294
 
12.8%
473
 
3.2%
525
 
1.1%
628
 
1.2%
756
 
2.4%
855
 
2.4%
975
 
3.3%
10118
 
5.2%
ValueCountFrequency (%)
12454
19.8%
11135
 
5.9%
10118
 
5.2%
975
 
3.3%
855
 
2.4%
756
 
2.4%
628
 
1.2%
525
 
1.1%
473
 
3.2%
3294
12.8%

Piperacillin-tazobactam
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9349629
Minimum0
Maximum14
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:10.286560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q110
median13
Q314
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.045904362
Coefficient of variation (CV)0.1714210911
Kurtosis-0.5152076478
Mean11.9349629
Median Absolute Deviation (MAD)1
Skewness-0.6642081827
Sum27343
Variance4.18572466
MonotonicityNot monotonic
2021-06-07T18:42:10.478219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
14742
32.4%
13481
21.0%
10283
 
12.4%
11232
 
10.1%
9226
 
9.9%
12174
 
7.6%
8139
 
6.1%
78
 
0.3%
65
 
0.2%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
65
 
0.2%
78
 
0.3%
8139
 
6.1%
9226
 
9.9%
10283
 
12.4%
11232
 
10.1%
12174
 
7.6%
13481
21.0%
14742
32.4%
ValueCountFrequency (%)
14742
32.4%
13481
21.0%
12174
 
7.6%
11232
 
10.1%
10283
 
12.4%
9226
 
9.9%
8139
 
6.1%
78
 
0.3%
65
 
0.2%
01
 
< 0.1%

Aztreonam
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.33347883
Minimum2
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:10.672223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q111
median11
Q311
95-th percentile11
Maximum11
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.911722305
Coefficient of variation (CV)0.1850027795
Kurtosis7.920233407
Mean10.33347883
Median Absolute Deviation (MAD)0
Skewness-2.983663753
Sum23674
Variance3.654682172
MonotonicityNot monotonic
2021-06-07T18:42:10.851721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
111974
86.2%
552
 
2.3%
839
 
1.7%
939
 
1.7%
1037
 
1.6%
334
 
1.5%
733
 
1.4%
629
 
1.3%
228
 
1.2%
426
 
1.1%
ValueCountFrequency (%)
228
 
1.2%
334
 
1.5%
426
 
1.1%
552
 
2.3%
629
 
1.3%
733
 
1.4%
839
 
1.7%
939
 
1.7%
1037
 
1.6%
111974
86.2%
ValueCountFrequency (%)
111974
86.2%
1037
 
1.6%
939
 
1.7%
839
 
1.7%
733
 
1.4%
629
 
1.3%
552
 
2.3%
426
 
1.1%
334
 
1.5%
228
 
1.2%

Ceftriaxone
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.577477084
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:11.034220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median10
Q310
95-th percentile10
Maximum10
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.437033246
Coefficient of variation (CV)0.1500429845
Kurtosis11.79385528
Mean9.577477084
Median Absolute Deviation (MAD)0
Skewness-3.573135714
Sum21942
Variance2.065064549
MonotonicityNot monotonic
2021-06-07T18:42:11.231221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
102060
89.9%
352
 
2.3%
837
 
1.6%
933
 
1.4%
532
 
1.4%
429
 
1.3%
729
 
1.3%
619
 
0.8%
ValueCountFrequency (%)
352
 
2.3%
429
 
1.3%
532
 
1.4%
619
 
0.8%
729
 
1.3%
837
 
1.6%
933
 
1.4%
102060
89.9%
ValueCountFrequency (%)
102060
89.9%
933
 
1.4%
837
 
1.6%
729
 
1.3%
619
 
0.8%
532
 
1.4%
429
 
1.3%
352
 
2.3%

Ceftaroline
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.16542994
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:11.411219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q111
median12
Q312
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.618709127
Coefficient of variation (CV)0.1449750824
Kurtosis11.52951142
Mean11.16542994
Median Absolute Deviation (MAD)0
Skewness-3.271269091
Sum25580
Variance2.620219236
MonotonicityNot monotonic
2021-06-07T18:42:11.603721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
121288
56.2%
11756
33.0%
1057
 
2.5%
947
 
2.1%
838
 
1.7%
425
 
1.1%
323
 
1.0%
621
 
0.9%
721
 
0.9%
513
 
0.6%
ValueCountFrequency (%)
22
 
0.1%
323
 
1.0%
425
 
1.1%
513
 
0.6%
621
 
0.9%
721
 
0.9%
838
 
1.7%
947
 
2.1%
1057
 
2.5%
11756
33.0%
ValueCountFrequency (%)
121288
56.2%
11756
33.0%
1057
 
2.5%
947
 
2.1%
838
 
1.7%
721
 
0.9%
621
 
0.9%
513
 
0.6%
425
 
1.1%
323
 
1.0%

Cefepime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.14229594
Minimum0
Maximum15
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:11.787221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median11
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.625339364
Coefficient of variation (CV)0.2356192457
Kurtosis1.271686137
Mean11.14229594
Median Absolute Deviation (MAD)0
Skewness-0.7818242383
Sum25527
Variance6.892406779
MonotonicityNot monotonic
2021-06-07T18:42:11.976220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
111251
54.6%
15379
 
16.5%
1392
 
4.0%
489
 
3.9%
1079
 
3.4%
1279
 
3.4%
1469
 
3.0%
962
 
2.7%
860
 
2.6%
758
 
2.5%
Other values (5)73
 
3.2%
ValueCountFrequency (%)
01
 
< 0.1%
21
 
< 0.1%
38
 
0.3%
489
3.9%
523
 
1.0%
640
1.7%
758
2.5%
860
2.6%
962
2.7%
1079
3.4%
ValueCountFrequency (%)
15379
 
16.5%
1469
 
3.0%
1392
 
4.0%
1279
 
3.4%
111251
54.6%
1079
 
3.4%
962
 
2.7%
860
 
2.6%
758
 
2.5%
640
 
1.7%

Ampicillin-sulbactam
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size132.1 KiB
13
1202 
12
901 
11
 
113
10
 
53
9
 
22

Length

Max length2
Median length2
Mean length1.990397206
Min length1

Characters and Unicode

Total characters4560
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
131202
52.5%
12901
39.3%
11113
 
4.9%
1053
 
2.3%
922
 
1.0%

Length

2021-06-07T18:42:12.357719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-07T18:42:12.506720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
131202
52.5%
12901
39.3%
11113
 
4.9%
1053
 
2.3%
922
 
1.0%

Most occurring characters

ValueCountFrequency (%)
12382
52.2%
31202
26.4%
2901
 
19.8%
053
 
1.2%
922
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4560
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12382
52.2%
31202
26.4%
2901
 
19.8%
053
 
1.2%
922
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common4560
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
12382
52.2%
31202
26.4%
2901
 
19.8%
053
 
1.2%
922
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12382
52.2%
31202
26.4%
2901
 
19.8%
053
 
1.2%
922
 
0.5%

Ceftazidime-avibactam
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.854648625
Minimum0
Maximum12
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:12.667721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q37
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.206243423
Coefficient of variation (CV)0.3768361799
Kurtosis1.645016745
Mean5.854648625
Median Absolute Deviation (MAD)1
Skewness1.052905226
Sum13413
Variance4.86751004
MonotonicityNot monotonic
2021-06-07T18:42:12.855219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4490
21.4%
5485
21.2%
6431
18.8%
7371
16.2%
8170
 
7.4%
12144
 
6.3%
3106
 
4.6%
940
 
1.7%
133
 
1.4%
218
 
0.8%
Other values (2)3
 
0.1%
ValueCountFrequency (%)
02
 
0.1%
133
 
1.4%
218
 
0.8%
3106
 
4.6%
4490
21.4%
5485
21.2%
6431
18.8%
7371
16.2%
8170
 
7.4%
940
 
1.7%
ValueCountFrequency (%)
12144
 
6.3%
101
 
< 0.1%
940
 
1.7%
8170
 
7.4%
7371
16.2%
6431
18.8%
5485
21.2%
4490
21.4%
3106
 
4.6%
218
 
0.8%

Ceftobiprole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.577040594
Minimum0
Maximum11
Zeros173
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:13.042220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median11
Q311
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.40953917
Coefficient of variation (CV)0.3560117697
Kurtosis2.823983894
Mean9.577040594
Median Absolute Deviation (MAD)0
Skewness-2.137213192
Sum21941
Variance11.62495735
MonotonicityNot monotonic
2021-06-07T18:42:13.233219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111912
83.5%
0173
 
7.6%
267
 
2.9%
343
 
1.9%
824
 
1.0%
720
 
0.9%
419
 
0.8%
69
 
0.4%
108
 
0.3%
98
 
0.3%
Other values (2)8
 
0.3%
ValueCountFrequency (%)
0173
7.6%
12
 
0.1%
267
 
2.9%
343
 
1.9%
419
 
0.8%
56
 
0.3%
69
 
0.4%
720
 
0.9%
824
 
1.0%
98
 
0.3%
ValueCountFrequency (%)
111912
83.5%
108
 
0.3%
98
 
0.3%
824
 
1.0%
720
 
0.9%
69
 
0.4%
56
 
0.3%
419
 
0.8%
343
 
1.9%
267
 
2.9%

Imipenem
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.353120908
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:13.410721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median5
Q310
95-th percentile10
Maximum10
Range6
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.629636698
Coefficient of variation (CV)0.413912585
Kurtosis-1.599932348
Mean6.353120908
Median Absolute Deviation (MAD)1
Skewness0.474771394
Sum14555
Variance6.914989164
MonotonicityNot monotonic
2021-06-07T18:42:13.588221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
41036
45.2%
10647
28.2%
5249
 
10.9%
9119
 
5.2%
6115
 
5.0%
765
 
2.8%
860
 
2.6%
ValueCountFrequency (%)
41036
45.2%
5249
 
10.9%
6115
 
5.0%
765
 
2.8%
860
 
2.6%
9119
 
5.2%
10647
28.2%
ValueCountFrequency (%)
10647
28.2%
9119
 
5.2%
860
 
2.6%
765
 
2.8%
6115
 
5.0%
5249
 
10.9%
41036
45.2%

Ceftolozane-tazobactam
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.218245308
Minimum0
Maximum12
Zeros174
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:13.770221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q312
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.585808841
Coefficient of variation (CV)0.4363229262
Kurtosis-0.3197667993
Mean8.218245308
Median Absolute Deviation (MAD)3
Skewness-0.6264240849
Sum18828
Variance12.85802504
MonotonicityNot monotonic
2021-06-07T18:42:13.954221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
12813
35.5%
6328
14.3%
7267
 
11.7%
5234
 
10.2%
8183
 
8.0%
0174
 
7.6%
9117
 
5.1%
1170
 
3.1%
1060
 
2.6%
443
 
1.9%
ValueCountFrequency (%)
0174
7.6%
32
 
0.1%
443
 
1.9%
5234
10.2%
6328
14.3%
7267
11.7%
8183
8.0%
9117
 
5.1%
1060
 
2.6%
1170
 
3.1%
ValueCountFrequency (%)
12813
35.5%
1170
 
3.1%
1060
 
2.6%
9117
 
5.1%
8183
 
8.0%
7267
 
11.7%
6328
14.3%
5234
 
10.2%
443
 
1.9%
32
 
0.1%

Doripenem
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.678306416
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:14.129219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median4
Q310
95-th percentile10
Maximum10
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.129372876
Coefficient of variation (CV)0.5511102513
Kurtosis-1.634471497
Mean5.678306416
Median Absolute Deviation (MAD)1
Skewness0.4865540223
Sum13009
Variance9.792974598
MonotonicityNot monotonic
2021-06-07T18:42:14.311725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
31141
49.8%
10643
28.1%
4184
 
8.0%
9108
 
4.7%
875
 
3.3%
753
 
2.3%
545
 
2.0%
642
 
1.8%
ValueCountFrequency (%)
31141
49.8%
4184
 
8.0%
545
 
2.0%
642
 
1.8%
753
 
2.3%
875
 
3.3%
9108
 
4.7%
10643
28.1%
ValueCountFrequency (%)
10643
28.1%
9108
 
4.7%
875
 
3.3%
753
 
2.3%
642
 
1.8%
545
 
2.0%
4184
 
8.0%
31141
49.8%

Ceftazidime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.19947621
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:14.785720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median12
Q312
95-th percentile12
Maximum12
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.773635077
Coefficient of variation (CV)0.1583676811
Kurtosis6.728214845
Mean11.19947621
Median Absolute Deviation (MAD)0
Skewness-2.693767124
Sum25658
Variance3.145781385
MonotonicityNot monotonic
2021-06-07T18:42:14.969719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
121641
71.6%
11300
 
13.1%
10110
 
4.8%
651
 
2.2%
746
 
2.0%
945
 
2.0%
438
 
1.7%
831
 
1.4%
523
 
1.0%
36
 
0.3%
ValueCountFrequency (%)
36
 
0.3%
438
 
1.7%
523
 
1.0%
651
 
2.2%
746
 
2.0%
831
 
1.4%
945
 
2.0%
10110
 
4.8%
11300
 
13.1%
121641
71.6%
ValueCountFrequency (%)
121641
71.6%
11300
 
13.1%
10110
 
4.8%
945
 
2.0%
831
 
1.4%
746
 
2.0%
651
 
2.2%
523
 
1.0%
438
 
1.7%
36
 
0.3%

MeroRPX7009_fixed8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.889567874
Minimum0
Maximum12
Zeros176
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size18.0 KiB
2021-06-07T18:42:15.143229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q36
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.585820003
Coefficient of variation (CV)0.9219070394
Kurtosis0.2089114259
Mean3.889567874
Median Absolute Deviation (MAD)0
Skewness1.238544066
Sum8911
Variance12.8581051
MonotonicityNot monotonic
2021-06-07T18:42:15.342220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
21170
51.1%
12214
 
9.3%
0176
 
7.7%
1140
 
6.1%
6113
 
4.9%
7113
 
4.9%
389
 
3.9%
864
 
2.8%
562
 
2.7%
1151
 
2.2%
Other values (3)99
 
4.3%
ValueCountFrequency (%)
0176
 
7.7%
1140
 
6.1%
21170
51.1%
389
 
3.9%
434
 
1.5%
562
 
2.7%
6113
 
4.9%
7113
 
4.9%
864
 
2.8%
942
 
1.8%
ValueCountFrequency (%)
12214
9.3%
1151
 
2.2%
1023
 
1.0%
942
 
1.8%
864
 
2.8%
7113
4.9%
6113
4.9%
562
 
2.7%
434
 
1.5%
389
3.9%

Interactions

2021-06-07T18:41:35.058612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:35.261616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:35.451114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:35.638612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:35.831614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.020613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.211116image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.413114image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.607422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.795923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:36.988926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:37.179922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:37.363423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:37.553923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:37.744929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:37.944422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:38.135923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:38.331922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:38.527923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:38.733922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:38.934922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:39.250423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:39.447423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:39.651924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:39.851920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.052923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.248923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.430922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.620423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.802422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:40.991424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:41.174923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:41.362423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:41.556422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:41.739423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:41.924923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:42.113423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:42.305923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:42.491922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:42.684422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:42.874422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:43.071919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:43.261422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:43.458423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:43.652420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:43.850422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:44.059422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:44.253422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:44.455923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:44.652923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:44.862419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:45.054923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:45.250922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:45.575920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:45.772423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:45.958422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:46.162922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:46.353920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:46.551422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:46.751423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:46.944423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:47.134423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:47.322420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:47.516422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:47.706923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:47.896422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:48.091923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:48.297423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:48.485923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:48.684922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:48.874422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:49.070422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:49.268919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:49.474922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:49.666919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:49.865923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:50.072923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:50.267420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:50.470863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:50.659863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:50.859863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:51.047863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:51.253863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:51.443364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:51.638359image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:51.834863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:52.028363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:52.225363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:52.422362image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:52.615862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:52.810364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:53.013863image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:53.212473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:53.581472image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:53.767473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:53.955973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:54.151973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:54.355470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:54.553521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:54.746522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:54.935026image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:55.126522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:55.317023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:55.502522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:55.703520image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:55.899023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:56.104022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:56.294024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:56.488022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:56.692022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:56.889522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:57.084022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:57.285023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:57.482522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:57.676524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:57.889022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:58.077523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:58.273522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:58.475523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:58.678523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:58.876523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:59.071522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:59.282022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:59.488522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:59.682022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:41:59.874522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:00.074523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:00.270522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:00.467022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:00.671524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:00.867521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:01.082023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:01.291023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:01.481522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:01.676523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:01.871519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:02.081024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:02.300023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:02.499526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:02.698523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:02.894522image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:03.102523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:03.525335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:03.733334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:03.919334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:04.110836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:04.303335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:04.497335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:04.684835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:04.875335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:05.074335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:05.280335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:05.469835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:05.659835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:05.850335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:06.047836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:06.238335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:06.430835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:06.631832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:06.832335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:07.039335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:07.234668image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:07.438167image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:07.634670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:07.834560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:08.026061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:08.228061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:08.422560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-06-07T18:42:08.616060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-06-07T18:42:15.551220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-07T18:42:15.826221image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-07T18:42:16.105220image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-07T18:42:16.379721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-07T18:42:08.951558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-07T18:42:09.249057image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

NameMeropenemPiperacillin-tazobactamAztreonamCeftriaxoneCeftarolineCefepimeAmpicillin-sulbactamCeftazidime-avibactamCeftobiproleImipenemCeftolozane-tazobactamDoripenemCeftazidimeMeroRPX7009_fixed8
0Sentry-2016-933582283334104345342
1Sentry-2016-934664283334104244342
2Sentry-2016-93482912131110121112611101210122
3Sentry-2016-93492512131110121112811101210123
4Sentry-2016-93495451311101211124114105123
5Sentry-2016-93548218234494345342
6Sentry-2016-935518111311101211121211912101211
7Sentry-2016-9355211013111012111212111012101211
8Sentry-2016-9355722101110121112511473122
9Sentry-2016-9356562103464125345352

Last rows

NameMeropenemPiperacillin-tazobactamAztreonamCeftriaxoneCeftarolineCefepimeAmpicillin-sulbactamCeftazidime-avibactamCeftobiproleImipenemCeftolozane-tazobactamDoripenemCeftazidimeMeroRPX7009_fixed8
2281Sentry-2018-10876952101110111113411563112
2282Sentry-2018-1087702491110111012511463112
2283Sentry-2018-10877233111110111413611483122
2284Sentry-2018-10877572101110111112411453112
2285Sentry-2018-10877672101110111313411453112
2286Sentry-2018-10877892121110111513511483122
2287Sentry-2018-10878022101110111112411463112
2288Sentry-2018-10878032121110111313311473121
2289Sentry-2018-10878242101110111213411453122
2290Sentry-2018-10878292111110111212411463112